And Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

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Article The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

David Mengen 1,*, Carsten Montzka 1 , Thomas Jagdhuber 2,3 , Anke Fluhrer 2,3 , Cosimo Brogi 1 , Stephani Baum 4, Dirk Schüttemeyer 5, Bagher Bayat 1 , Heye Bogena 1 , Alex Coccia 6, Gerard Masalias 6, Verena Trinkel 4, Jannis Jakobi 1, François Jonard 1,7 , Yueling Ma 1, Francesco Mattia 8, Davide Palmisano 8 , Uwe Rascher 4 , Giuseppe Satalino 8, Maike Schumacher 9, Christian Koyama 10, Marius Schmidt 1 and Harry Vereecken 1

1 Forschungszentrum Jülich, Institute of Bio-and Geosciences: Agrosphere (IBG-3), 52428 Jülich, Germany; [email protected] (C.M.); [email protected] (C.B.); [email protected] (B.B.); [email protected] (H.B.); [email protected] (J.J.); [email protected] (F.J.); [email protected] (Y.M.); [email protected] (M.S.); [email protected] (H.V.) 2 German Aerospace Center, Microwaves and Radar Institute, 82234 Wessling, Germany; [email protected] (T.J.); [email protected] (A.F.) 3 Institute of Geography, University of Augsburg, 86135 Augsburg, Germany 4 Forschungszentrum Jülich, Institute of Bio- and Geosciences: Plant Sciences (IBG-2), 52428 Jülich, Germany; [email protected] (S.B.); [email protected] (V.T.); [email protected] (U.R.) 5 Mission Science Division, European Space Agency, 2201 Noordwijk, The Netherlands; [email protected] 6 Metasensing BV, 2201 Noordwijk, The Netherlands; [email protected] (A.C.); [email protected] (G.M.) 7 Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium Citation: Mengen, D.; Montzka, C.; 8 Consiglio Nazionale delle Ricerche (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Jagdhuber, T.; Fluhrer, A.; Brogi, C.; 70126 Bari, Italy; [email protected] (F.M.); [email protected] (D.P.); [email protected] (G.S.) Baum, S.; Schüttemeyer, D.; Bayat, B.; 9 Geodesy and Surveying, Aalborg University, 9220 Aalborg, Denmark; [email protected] Bogena, H.; Coccia, A.; et al. The 10 School of Science and Engineering, Tokyo Denki University, Tokyo 120-8551, Japan; [email protected] SARSense Campaign: Air- and * Correspondence: [email protected] Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Abstract: With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob- Parameters in Agriculture. Remote serving System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite Sens. 2021, 13, 825. https://doi.org/ missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial reso- 10.3390/rs13040825 lution will become available. The SARSense campaign was conducted between June and August

Received: 25 January 2021 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Accepted: 18 February 2021 Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by Published: 23 February 2021 extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR Publisher’s Note: MDPI stays neutral data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals with regard to jurisdictional claims in regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency published maps and institutional affil- approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify iations. specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

Keywords: ROSE-L; soil moisture; plant parameters; L-band; C-band; SAR; airborne campaign Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons With the increasing impact of human activities and the effects of climate change on Attribution (CC BY) license (https:// hydrological systems worldwide, appropriate and adapted management and mitigation creativecommons.org/licenses/by/ concepts are required [1–4]. This is particularly true with regard to the goal of using 4.0/).

Remote Sens. 2021, 13, 825. https://doi.org/10.3390/rs13040825 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 825 2 of 29

natural resources more effectively and sustainably in the future [5]. Since soil moisture and water-related vegetation conditions are key parameters in this context, they need to be assessed and monitored at both global and local scales. By providing global data with high temporal and spatial resolution, modern Earth Observation (EO) satellites have become a key technology in this field, whose importance will significantly increase in the future [6–8]. Radar Observing System for Europe L-band SAR (ROSE-L), as one of the Copernicus High Priority Candidate satellite missions is foreseen to be able to target the abovemen- tioned objectives. The mission was first agreed on at the European Space Agency (ESA) ministerial conference Space19+ in Seville in November 2019 and was contractually signed by ESA and Thales Alenia Space later in December 2020 as part of the Fourth ESA Coper- nicus Space Component Program. With a scheduled launch in 2028, the two satellites, carrying a quad-polarimetric L-band SAR, are designed for collecting valuable data, especially for various research and applications in the field of soil moisture, land cover mapping, maritime surveillance, and natural and anthropogenic hazards [9]. A third add-on satellite is currently under discussion for bi-static records (ROSE-L+). In synergy with the existing Sentinel-1 A/B SAR mission, ROSE-L will enhance the European radar imaging capacity by increasing the frequency of successive radar data collections. In this regard, it will also enhance the possibilities for using soil and plant parameter retrieval based on change detection methods (e.g., alpha approximation and interferometry methods). Since L-band wavelength is able to penetrate through various media like vegetation or dry snow, it additionally provides unique information that cannot be obtained using higher frequency bands like the Sentinel-1 C-band and vice versa [10–12]. In combination, a quasi multi- band space-borne radar product can be obtained, which is currently only available using individual airborne flight campaigns [9]. The joint NASA-ISRO SAR (NISAR) satellite mission planned by NASA and Indian Space Research Organization (ISRO) for 2022 can be seen as a potential precursor, carrying both an L- and S-band SAR [13]. In the course of the planning phase of the ROSE-L satellite mission, the potential of L-band SAR data for the proposed applications and the synergy effects from combining L- and C-band SAR data need to be explored. Such information will help to optimize ROSE-L regarding its synergies with the Sentinel-1 mission as well as with other radar satellite missions, e.g., RADARSAT Constellation Mission (RCM), NISAR, Advanced Land Observing Satellite (ALOS-2/4), Satélite Argentino de Observación con Microondas (SAOCOM), TerraSAR-X/TanDEM-X, Paz, and optical satellite missions, e.g., Sentinel-2 and the Landsat series. Various flight campaigns were conducted in the past to unlock the information content of SAR data, particularly for measuring environmental parameters over agricultural and forested areas as • The AgriSAR 2006 campaign was conducted over the Durable Environmental Multidis- ciplinary Monitoring Information Network (DEMMIN) agricultural site in Germany recorded C- and L-band SAR observations and multispectral images in preparation of Sentinel-1 and Sentinel-2 satellite missions [14]. • TropiSAR 2009 campaign was conducted over Nouragues, Paracou in French Guiana, with simultaneous P- and L-band SAR data recording, evaluating the potential of SAR for estimation of biomass over tropical forests [15]. • The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flight campaign was conducted between 2012 and 2015 using P-band SAR for polarimetric measurements over major North American biomes, especially focusing on root-zone soil moisture [16]. • The NASA-ISRO Airborne Synthetic Aperture Radar (ASAR) flight campaign in 2019 was conducted over different biomes in North America, investigating the potential of L- and S-band for environmental monitoring in the context of the upcoming NISAR satellite mission [17]. • The UAVSAR AM-PM campaign in 2019 was conducted over different biomes in the Southeastern United States in preparation for the upcoming NISAR satellite mission, using L-band SAR with alternating morning and evening acquisition times [18]. Remote Sens. 2021, 13, 825 3 of 29

Soil moisture, being one of the key parameters within the hydrological cycle, is of high interest for a wide range of research, e.g., for weather and climate research, hydrological modeling, and water resources management [19–21]. In addition, as soil moisture directly affects agricultural production, e.g., by water stress and irrigation demand, it is a crucial parameter for agricultural management decisions and practices at a local scale [22]. As polarimetric SAR data is capable of estimating soil moisture for various environmental and vegetation conditions, the potential of this technology has already been assessed, and various methods are currently employed for soil moisture retrieval [22–24]. The SAR backscatter coefficient sigma nought (σ0) is directly proportional to the effective scattering area of an illuminated surface, and is affected both by surface parameters, e.g., soil moisture content (mv), soil texture, surface roughness, and vegetation cover, as well as observation system (instrument) parameters, e.g., frequency, polarization, and incidence angle (θ)[25–30]. Being affected by the vegetation cover, plant parameters like plant height and vegetation water content can also be inferred using SAR data [31–33]. Due to the different wavelengths, C- and L-band SAR differ in their sensitivity to soil and plant parameters, allowing more detailed parameter observations and monitoring when combined. In this context, the SARSense flight campaign was carried out between the 19 June and 9 August 2019 to investigate the potential and synergy effects of using full-polarized, multi-frequency SAR data regarding soil and plant parameter retrieval for bare soil and under various vegetation covers [34]. The campaign was conducted on the Terrestrial Environmental Observatories (TERENO) field research site, named Selhausen, located near Jülich, Germany [35]. Simultaneous to the L- and C-band SAR observations, in situ measurements and UAS mapping were performed. The aim of this contribution is to characterize the study area (Section2), to describe the SAR, UAS, and in situ observation strategy and collected data (Section3), to inform about data pre-processing and applied methods (Section4), to present and discuss the main results with respect to the above- mentioned campaign objectives (Sections5 and6), as well as to publish the dataset for making them publicly available for further research in the community.

2. Study Area The Selhausen test site (50.865◦N, 6.447◦E) is an intensively cultivated area of about 1 km2 in the Eastern part of the Rur catchment in Germany. It is part of the Eifel/Lower Rhine Valley Observatory within the TERENO initiative, involving six Helmholtz Associa- tion Centers at four distinct observatories across Germany, aiming at the long-term and integrated observation of the effects of climate and global change on especially vulnerable terrestrial environments. This includes both the subsurface and land surface as well as the lower atmosphere and anthroposphere [36]. Within a multitemporal and multi-scale approach, the TERENO initiative provides real-time measurement platforms to monitor related environmental parameters and conduct controlled field experiments [37–39]. Being used as a soil moisture validation site for the microwave satellite missions Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and ALOS-2, multiple active and passive L-band airborne campaigns were conducted since 2010 at this test site [40–42]. Furthermore, it is a Committee on Earth Observation Satellites Land Product Validation Subgroup (CEOS LPV) Supersite for the validation of satellite-derived products and is part of the Integrated Carbon Observation System (ICOS) program (field 11), a European- wide, standardized measuring network of atmospheric greenhouse gas concentrations and exchange fluxes with terrestrial and marine ecosystems [43]. The Selhausen test site consists of 56 individual agricultural fields (Figure1; Table1) . The test site comprises a great diversity in agricultural cropping structure due to the prop- erty fragmentation between farmers and a heterogeneous subsurface. Representing the agricultural landuse of the Lower Rhine Embayment, winter wheat, sugar beet, winter barley, potato, silage maize and winter rapeseed are generally cultivated in rotation. Occa- sionally, cabbage, oat, and rye are cultivated while some fields are left bare or covered with grass or catch crops. Located in the tempered maritime climate zone, the mean annual tem- Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 28

with grass or catch crops. Located in the tempered maritime climate zone, the mean annual temperature and precipitation are 10.2 °C and 741 mm, respectively [35]. The major soil types are (gleyic) Luvisol and (gleyic) Cambisol with in majorly silty loam texture [35], having a high variability in the percentage of individual grain size classes in the uppermost 30 cm of soil (sand: 13–35%, silt = 52–70%, clay = 13–17%) [44,45]. Due to a weak inclination of the terrain (<4°), colluvial sediments are deposited on parts of the lower areas. Underneath, eolian sediments from Pleistocene and Holocene with a thickness of up to 2 m are placed on top of Quaternary, mostly fluvial sediments from the Rhine/Meuse Remote Sens. 2021, 13, 825 river and Rur river system [45,46]. In fact, recent studies on remote and proximal sensing4 of 29 of the leaf area index (LAI), as well as crop modelling revealed a historic river channel system on the test site, influencing the crop development during the growing season, es- peciallyperature at and dry precipitationsoil conditions are [46–48]. 10.2 ◦C In and this 741 regard, mm, respectivelyalso the surface [35]. soil The moisture major soil content types (SSMC)are (gleyic) is highly Luvisol variable and (gleyic) across Cambisol the site [44]. with Furthermore, in majorly silty a loamrecent texture study [developed35], having a a high-resolution,high variability inmeter-scaled the percentage soil ofmap individual with 18 soil grain types size for classes the test in the site uppermost using electromag- 30 cm of neticsoil (sand:induction 13–35%, (EMI) silt measurements = 52–70%, clay on = 51 13–17%) agricultural [44,45 fields]. Due in to the a weak Selhausen inclination area [45]. of the terrainThe (<4 flight◦), colluvial campaign sediments was carried are out deposited during ona severe parts ofdrought the lower that areas.affected Underneath, broad re- gionseolian from sediments North-East from to Pleistocene Western Europe and Holocene in 2019 [49]. with The a thickness monthly of temperature up to 2 m are was placed sig- nificantlyon top of Quaternary,above and precipitation mostly fluvial significantly sediments frombelow the the Rhine/Meuse long-term averages river and (Figure Rur river 2), resultingsystem [ 45in ,low46]. to In very fact, low recent SSMC studies values, on with remote a mean and of proximal 8 vol.% sensingin June and of the 17 leafvol.% area in August.index (LAI), Due asto wellthe asunderlying crop modelling historic revealed river channels, a historic the river SSMC channel was system highly on variable the test acrosssite, influencing the Selhausen the cropsite, developmentwith higher SSMC during found the growing in areas, season, with deeper especially river at channel dry soil sedimentsconditions [46,48]. [46–48 ].This In thiseffect regard, was also also observable the surface in soil the moisturemultispectral content and (SSMC) thermal is record- highly ings.variable To cope across with the these site [drought44]. Furthermore, conditions, a pa recentrts of study the test developed site were a irrigated high-resolution, during themeter-scaled investigated soil period. map with Irrigation 18 soil was types either for the performed test site usingby the electromagnetic farmers or during induction a specific(EMI) experiment. measurements on 51 agricultural fields in the Selhausen area [45].

FigureFigure 1.1. Map ofof thethe SelhausenSelhausen testtest sitesite andand airborneairborne ﬂightflight tracks tracks (left) (left) as as well well as as the the individual individ- crop typesual crop and types ﬁeld and IDs (right).field IDs (right).

Table 1. Overview of crop types and ﬁeld IDs for the Selhausen test site.

Crop Type Field ID bare soil F09a, F10 barley F15, F16, F17b, F20, F22a, F27, F33, F35, F36, F39, F48b cabbage F54 oat F23b, F25, F30, F56 potato F11, F14b rye F18ab, F49b, F46 silage maize F03, F06, F09b, F13a, F24b, F41, F42, F44a, F51b, F55 sugar beet F01, F04, F14a, F21, F28, F40, F44b, F47 wheat F05, F07, F8_24, F12, F13ba, F17a, F22cb, F23a, F37, F38, F50c, F51a winter rapeseed F53

The flight campaign was carried out during a severe drought that affected broad regions from North-East to Western Europe in 2019 [49]. The monthly temperature was significantly above and precipitation significantly below the long-term averages Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 28

Table 1. Overview of crop types and field IDs for the Selhausen test site.

Crop Type Field ID Remote Sens. 2021, 13, 825bare soil F09a, F10 5 of 29 barley F15, F16, F17b, F20, F22a, F27, F33, F35, F36, F39, F48b cabbage F54 oat F23b, F25, F30, F56 (Figure2), resulting in low to very low SSMC values, with a mean of 8 vol.% in June potato F11, F14b and 17 vol.% in August. Due to the underlying historic river channels, the SSMC was rye F18ab, F49b, F46 highly variable across the Selhausen site, with higher SSMC found in areas, with deeper silage maize river channel F03, sediments F06, F09b, [46 F13a,,48]. ThisF24b, effect F41, wasF42, alsoF44a, observable F51b, F55 in the multispectral and sugar beet thermal recordings. F01, To F04, cope F14a, with F21, these F28, drought F40, F44b, conditions, F47 parts of the test site were wheat irrigated F05, duringF07, F8_24, the investigated F12, F13ba, period.F17a, F22cb, Irrigation F23a, was F37, either F38, F50c, performed F51a by the farmers or winter rapeseedduring a speciﬁc experiment. F53

Figure 2. Precipitation and temperature measurements of 2019 compared to the long-term average (29 years). Within the Figure 2. Precipitation and temperature measurements of 2019 compared to the long-term average period of the SARSense campaign, it was both hotter and dryer than average. (29 years). Within the period of the SARSense campaign, it was both hotter and dryer than average. 3. Data 3. Data For the SARSense campaign, airborne C- and L-band recordings were performed on For the SARSense campaign, airborne C- and L-band recordings were performed on the 19, 21, 25, and 27 of June and 8 and 9 of August 2019, while simultaneously measuring the 19, 21, 25, and 27 of June and 8 and 9 of August 2019, while simultaneously measuring in situ soil temperature, soil moisture, bulk electrical conductivity, pore water electrical in situ soil temperature, soil moisture, bulk electrical conductivity, pore water electrical conductivity, dielectric permittivity, as well as vegetation height. The explicit measurement conductivity, dielectric permittivity, as well as vegetation height. The explicit measure- of vegetation parameters in the field and in the laboratory was conducted on the 25 June ment of vegetation parameters in the field and in the laboratory was conducted on the 25 and 7 August 2019. Among others, the fresh weight, dry weight and water content of leaves June and 7 August 2019. Among others, the fresh weight, dry weight and water content and stems were measured as well as the leaf area, phenology code, plant height and leaf of leaves and stems were measured as well as the leaf area, phenology code, plant height chlorophyll concentration. An overview of all measured soil and vegetation parameters and leaf chlorophyll concentration. An overview of all measured soil and vegetation pa- can be found in the AppendixA (Table1). For a comparison with space-borne SAR data, 58 rameters can beSentinel-1 found in the (VV Appendix and VH) andA (Tab 6 ALOS-2le A1). For scenes a comparison (HH and HV) with were space-borne acquired for the period SAR data, 58 Sentinel-1from the (VV 1 June and to VH) 31 August and 6 2019.ALOS-2 In addition,scenes (HH cosmic-ray and HV) neutron were acquired sensing using a mobile for the period fromcosmic-ray the 1 June rover to 31 was August conducted 2019. onIn addition, the 27 June cosmic-ray and 8 August neutron 2019. sensing Using UASs, RGB using a mobile mapscosmic-ray were rover taken was on the conducte 17 andd 26on June, the 27 the June 3 and and 25 8 August July, and 2019. 8 August Using 2019 as well as UASs, RGB mapstemperature were taken and on multispectralthe 17 and 26 observationsJune, the 3 and taken 25 July, on the and 26 8 and August 27 June 2019 2019. as well as temperatureAs and the Selhausenmultispectral test observations site is part of taken the TERENO on the 26 and and ICOS 27 June program, 2019. numerous mea- As the Selhausensuring test stations site is for part climate, of the soil,TERENO and vegetation and ICOS parametersprogram, numerous are permanently meas- installed and uring stations forrunning/monitoring. climate, soil, and vegetation This includes parameters two eddy are covariance permanently and installed three climate and stations, mea- running/monitoring.suring This fluxes includes and meteorological two eddy covariance parameters and respectively,three climate a stations, groundwater meas- well measuring uring fluxes andwater meteorological level, conductivity, parameters and respectively, temperature, a groundwater as well as four well automated measuring closed dynamic water level, conductivity,chambers for and measuring temperature, soil COas 2wellemissions. as four Inautomated addition, closed 18 lysimeters dynamic continuously de- chambers for measuringtermine water soil CO balance2 emissions. parameters In addition, (soil matrix 18 lysimeters potential, continuously soil temperature, de- soil heat flux, termine water balanceand soil parameters water content). (soil Twomatrix rhizotron potential, facilities soil temperature, enable root growthsoil heat observations flux, and soil and soil water content).moisture Two monitoring rhizotron with facilities ground-penetrating enable root growth radar andobservations borehole and cameras soil in both lateral moisture monitoringand vertical with grou directionsnd-penetrating during a radar crop growingand borehole cycle. cameras in both lateral and vertical directions during a crop growing cycle. 3.1. C- and L-Band Airborne SAR The C- and L-band SAR data were acquired and processed by the company MetaSens- ing. The carrier was a Cessna 208 with left side-looking antennas having a nominal look ◦ ◦ ◦ angle of 45 , resulting in incidence angles ranging from 30 to 55 . The flight altitude was around 1620 m (Figure3). To cover the larger Selhausen agricultural area, three tracks were flown per campaign day, with two ascending (track A and B) and one descending (track C) flight track and about 20% overlap among adjacent scenes. The producer-side processing Remote Sens. 2021, 13, 825 6 of 29

steps of the radar data consist of range compression, global back-projection, geometric and radiometric calibration. Three (75 cm) corner reflectors were set up for calibration, but due to misalignment, they were only used for geometric calibration. For radiometric calibration, the C- and L-band images were calibrated among themselves, based on the estimated noise level of each data take. The first acquisition was taken as a reference value for both C- and L-band frequencies and from these, a relative noise level was calculated for each track to zero-out any temporal fluctuation. To minimize the mean offset between C-band airborne and space-borne datasets, the Sentinel-1 scene 20190620T055005a was Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 28 used to calculate a global calibration factor for matching the reflectivity histograms of both data sets for a common patch over a uniformly forested area. Based on empirical values found in the literature, the L-band airborne data was calibrated using a similar procedure. Afterresolution aggregating of 5 m by the 20 L-bandm by using data Terrain for all Observation missions, the with backscatter Progressive histogram Scans SAR over (TOP- the sameSAR). uniformly For GRDH, forested the resolution area was is calculated. resampled Then, into a a 10 calibration m by 10 m constant pixel spacing. for the airborneThe data L-bandwas obtained system using was estimatedthe Google such Earth that Engine its histogram (GEE) web mean platform, would already fall 2.5 dBbeing below pre-pro- that ofcessed the airborne by the Sentinel-1 C-band radar Toolbo [50x]. SNAP As no [54]. polarimetric The pre-processing calibration steps was performedconsist of thermal on the SARnoise data, removal, only theradiometric backscatter calibration, coefficients terrain of the correction four polarimetric using Shuttle channels Radar (VV, Topography VH, HV, andMission HH) (SRTM) are available. Version In this3.0 regard,Global the1 arc data second is not dataset suitable (SRTMGL1) for eigen- and and model-based converting polarimetricbackscatter values decomposition to decibels methods (dB) using without log furtherscaling processing.[55]. Additionally, The data speckle is provided filtering as 0 Singlewas performed Look Complex using (SLC)a focalσ medianin NetCDF filter filewith format. a kernel size of 3 by 3 pixels.

FigureFigure 3.3. CessnaCessna C208C208 carryingcarrying the SARSense radar setup.setup. The top top left left and and bottom bottom right right antennas antennas areare forfor L-band,L-band, thethe bottombottom leftleft andand toptop rightright antennas antennas for for C-band. C-band.

TableThe 2. Description quad-pol of (VV, the VH,SARSense HV, andC- and HH) L-band C-band radar SARsystem. sensor, used microstrip radio frequency (RF) antennas at a center frequency of 5400 MHz and transmitting a bandwidth Parameter C-Band L-Band of 200 MHz, a pulse repetition frequency of 1.89 KHz. The data were sampled at 50 MHz Antenna Geometry (cm) 32 × 13 33 × 33, 33 × 66 (Table2). The global geolocation accuracy in cross-range is 3.06 m and in slant-range 2.92 m based on averageAltitude displacement (m) as opposed to corner reﬂectors. 1620 The mean Noise Equivalent Sigma ZeroVelocity (NESZ) (Kn) was calculated over a body of standing ~130 water body in the north of everyNominal scene and look is − angle27.6 dB.(°) The quad-pol L-band SAR sensor 45 also used microstrip RF antennas and theMode same transmission parametersFrequency Modulated as the C-band. Continuous However, Wave-Full-Polar the transmission bandwidthPeak was Power limited (W) by the German authorities (Bundesnetzagentur) 3–10 to 50 MHz. For the datesActual 19, 21, PRF 25, and(kHz) 27 of June, the center frequency was 1.89 1400 MHz, whereas for the 8 andSampling 9 of August, frequency the center (MHz) frequency was 1300 MHz. The 50global geolocation accuracy in cross-range is 3.05 m and in slant-range 3.01 m based on the average displacement of Center frequency (MHz) 5400 1400/1300 corner reﬂectors. The mean NESZ is −34.8 dB and was computed on the same water body Transmitted bandwidth (MHz) 200 50 as the C-band. Azimuth bandwidth (MHz) 100 Beamwidth (Azim. × Elev.) (°) 10 × 35 40 × 40, 20 × 40 Ground range resolution (m) 0.9–1.3 3.6–5.2 Range pixel spacing (m) 1 Azimuth pixel spacing (m) 1 Incidence angle range (°) 35–55

Remote Sens. 2021, 13, 825 7 of 29

Table 2. Description of the SARSense C- and L-band radar system.

Parameter C-Band L-Band Antenna Geometry (cm) 32 × 13 33 × 33, 33 × 66 Altitude (m) 1620 Velocity (Kn) ~130 Nominal look angle (◦) 45 Mode Frequency Modulated Continuous Wave-Full-Polar Peak Power (W) 3–10 Actual PRF (kHz) 1.89 Sampling frequency (MHz) 50 Center frequency (MHz) 5400 1400/1300 Transmitted bandwidth (MHz) 200 50 Azimuth bandwidth (MHz) 100 Beamwidth (Azim. × Elev.) (◦) 10 × 35 40 × 40, 20 × 40 Ground range resolution (m) 0.9–1.3 3.6–5.2 Range pixel spacing (m) 1 Azimuth pixel spacing (m) 1 Incidence angle range (◦) 35–55

3.2. Sentinel-1 C-Band SAR The satellites Sentinel-1 A and Sentinel-1 B are imaging the Earth with a C-band SAR instrument using a center frequency of 5400 MHz. Sharing the same orbital plane, the two satellites have a combined exact revisit time of six days, being able to map the Earth´s surface independently from weather conditions and during both day and night time [51,52]. For the period from June 2019 to August 2019 a total of 58 Sentinel-1A/B dual-polarized (VV + VH) scenes in ascending and descending mode in Interferometric Wide-Swath Mode (IW) and Ground Range Detected High Resolution (GRDH) format [53] were acquired. To obtain the highest possible number of scenes, four different orbits were used, resulting in alternating incidence angles (Desc.: 43.1◦, Asc.: 30.1◦, Desc.: 34.6◦, Asc.: 40.1◦). The IW Mode captures three-sub-swaths, combining it into a 250 km swath with a spatial resolution of 5 m by 20 m by using Terrain Observation with Progressive Scans SAR (TOPSAR). For GRDH, the resolution is resampled into a 10 m by 10 m pixel spacing. The data was obtained using the Google Earth Engine (GEE) web platform, already being pre-processed by the Sentinel-1 Toolbox SNAP [54]. The pre-processing steps consist of thermal noise removal, radiometric calibration, terrain correction using Shuttle Radar Topography Mission (SRTM) Version 3.0 Global 1 arc second dataset (SRTMGL1) and converting backscatter values to decibels (dB) using log scaling [55]. Additionally, speckle ﬁltering was performed using a focal median ﬁlter with a kernel size of 3 by 3 pixels.

3.3. ALOS-2 L-Band SAR Six dual-polarized (HH + HV) scenes of ALOS-2 in Stripmap Fine Mode (SM3) with a range resolution of 9.1 m and azimuth resolution of 5.3 m for the period from June 2019 to August 2019 were selected, with a revisit time of two to seven days. The scenes were recorded from two ascending and one descending orbit with an incidence angle of 34◦ and 35◦ (ascending) as well as 37◦ (descending) at the Selhausen test site. The Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) sensor, operating with a center frequency of 1257.5 MHz and in Stripmap Fine Mode data is captured at 28 MHz bandwidth with a swath width of 70 km. The SLC data, at processing level 1.1, was provided by the Japan Aerospace Exploration Agency (JAXA) in cooperation with ESA. For further analysis, the data was radiometrically calibrated, resampled to ground range resolution of 10 m by 10 m, speckle ﬁltered (3 by 3 pixels) using a focal median ﬁlter and geolocated using the ESA toolbox SNAP. A visual comparison between the C- and L-band air- and space borne data are displayed in Figure4. Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 28

3.3. ALOS-2 L-Band SAR Six dual-polarized (HH + HV) scenes of ALOS-2 in Stripmap Fine Mode (SM3) with a range resolution of 9.1 m and azimuth resolution of 5.3 m for the period from June 2019 to August 2019 were selected, with a revisit time of two to seven days. The scenes were recorded from two ascending and one descending orbit with an incidence angle of 34° and 35° (ascending) as well as 37° (descending) at the Selhausen test site. The Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) sensor, operating with a center frequency of 1257.5 MHz and in Stripmap Fine Mode data is captured at 28 MHz bandwidth with a swath width of 70 km. The SLC data, at processing level 1.1, was provided by the Japan Aerospace Exploration Agency (JAXA) in cooperation with ESA. For further analysis, the data was radiometrically calibrated, resampled to ground range resolution of 10 m by 10 m, speckle filtered (3 by 3 pixels) using a focal median filter and geolocated using Remote Sens. 2021, 13, 825 the ESA toolbox SNAP. A visual comparison between the C- and L-band8 of 29 air- and space borne data are displayed in Figure 4.

FigureFigure 4. 4.Comparison Comparison of airborne of airborne C- and L-band C- and data L-band with Sentinel-1 data with and Sentinel-1 ALOS-2 over and the SelhausenALOS-2 over the Sel- testhausen site for test the site 21/22 for June. the 21/22 June. 3.4. UASs 3.4. InUASs the SARSense campaign, multiple UAS flights were performed, covering an area of 2 0.85 kmIn. the RGB SARSense images were campaign, captured by amultiple Mavic Pro UAS as flights well as 5-channelwere performed, multispectral covering an area and thermal infrared measurements were taken by respectively a Micasense RedEdge-M andof 0.85 a FLIR km². VUE RGB Pro Rimages 640 sensor were mounted captured on aby DJI a M600 Mavic UAS Pro (Figure UAS5 ).as The well images as 5-channel multi- werespectral georeferenced and thermal using AeroPointsinfrared measurements GPS ground control were devices taken [56 ].by The respectively individual a Micasense flightRedEdge-M plans were and created a FLIR using VUE DJI GroundStation Pro R 640 sensor Pro. mounted on a DJI M600 UAS (Figure 5). TheThe images Mavic were Pro UAS georeferenced carries a 12.7 megapixels using AeroPoints RGB camera GPS with ground an optical control distortion devices [56]. The ofindividual less than 1.5% flight on plans a 3-axis were gimbal crea [57ted]. During using theDJI image GroundStation acquisition, thePro. drone had an average flight speed of 40 km/h, covering the whole test site with a flight altitude of 120 m,The resulting Mavic in Pro a spatial UAS resolution carries <4a 12.7 cm. Usingmegapixels AgiSoft MetaShape,RGB camera orthomosaics with an foroptical distortion theof less whole than Selhausen 1.5% on test a site 3-axis were gimbal created from[57]. aDuring total of 580the nadir image images, acquisition, with a front the drone had an overlapaverage of flight 80% and speed side overlapof 40 km/h, of 60%. covering In total, fivethe RGBwhole orthomosaics test site with were a created, flight altitude of 120 documentingm, resulting the in Selhausena spatial testresolution site over the<4 cm. whole Using SARSense AgiSoft campaign MetaShape, period. During orthomosaics for the the standard procedure to generate orthomosaics, a Digital Elevation Model (DEM) was generatedwhole Selhausen from RGB test data site by a were structure-from-motion created from a procedure.total of 580 Characteristic nadir images, features with a front over- werelap of identified 80% and in multiple side overlap images andof 60%. by this In photogrammetric total, five RGB multi-view orthomosaics approach were the created, docu- 3Dmenting position the and Selhausen orientation oftest each site feature over is the retrieved. whole The SARSense resulting 3D campaign point cloud period. can During the thenstandard be converted procedure into a DEM,to generate which is providedorthomosaics, here with a a spatialDigital resolution Elevation of <8 Model cm. (DEM) was The DJI M600 was flown at an altitude of 120 m at 31 km/h with both sensors (multispectral and thermal infrared) taking nadir images every second, with the GPS position data being stored in every individual image. The FLIR VUE Pro R 640 is a radiometric thermal infrared camera with a spectral range between 7.5 to 13.5 µm, an accuracy of (±) 5 ◦C as well as a thermal sensitivity of 0.05 ◦C[58]. Equipped with a 13 mm lens, the camera has a 45◦ by 37◦ field of view with a sensor resolution of 640 by 512 pixels. The images were combined into an orthomosaic with Pix4D resulting in a spatial resolution of <40 cm. Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 29

generated from RGB data by a structure-from-motion procedure. Characteristic features were identified in multiple images and by this photogrammetric multi-view approach the 3D position and orientation of each feature is retrieved. The resulting 3D point cloud can then be converted into a DEM, which is provided here with a spatial resolution of <8 cm. The DJI M600 was flown at an altitude of 120 m at 31 km/h with both sensors (multispectral and thermal infrared) taking nadir images every second, with the GPS position data being stored in every individual image. The FLIR VUE Pro R 640 is a radiometric thermal infrared camera with a spectral range between 7.5 to 13.5 µm, an accuracy of (±) 5 °C as well as a thermal sensitivity of 0.05 °C [58]. Equipped with a 13 mm lens, the camera has a 45° by 37° field of view with a sensor resolution of 640 by 512 pixels. The images were combined into an orthomosaic with Pix4D resulting in a spatial resolution of <40 cm. The Micasense RedEdge-M is a 5-channel multispectral sensor, also containing a red edge and near infrared (NIR) bands besides the RGB bands. The spectral range of the individual bands can be found in Table 3. Furthermore, it is equipped with a downwelling light sensor, measuring the ambient light for each band to correct lighting changes during the flight, e.g., due to changing cloud cover. The sensor was calibrated before and after Remote Sens. 2021, 13, 825 every flight using the standard calibration panel. The data was combined to an 119 ofcm 29 resolution orthomosaic for each date with AgiSoft Metashape.

Figure 5. Normalized Difference Red-Edge (NDRE) index measured by the Micasense RedEdge-M multispectral sensor (left) and surface temperature (◦C) measured by the FLIR VUE Pro 640 thermal infrared camera (right) on the 27 June 2019. Figure 5. Normalized Difference Red-Edge (NDRE) index measured by the Micasense RedEdge-M multispectral sensor (left) and surface temperature (°C) measured by the FLIR VUE Pro 640 The Micasense RedEdge-M is a 5-channel multispectral sensor, also containing a red thermal infrared camera (right) on the 27 June 2019. edge and near infrared (NIR) bands besides the RGB bands. The spectral range of the Tableindividual 3. Spectral bands band can information be found infor Table the Micasense3. Furthermore, RedEdge-M. it is equipped with a downwelling light sensor, measuring the ambient light for each band to correct lighting changes during the ﬂight,Band e.g., Name due to changing Center cloud Wavelength cover. The sensor (nm) was calibrated Bandwidth before (nm) and after every ﬂightBlue using the standard calibration475 panel. The data was combined 20 to an 11 cm resolution orthomosaic for each date with AgiSoft Metashape. Green 560 20 Table 3. SpectralRed band information for the Micasense668 RedEdge-M. 10 Red Edge 717 10 Band Name Center Wavelength (nm) Bandwidth (nm) NIRBlue 840 475 40 20 Green 560 20 Red 668 10 Red Edge 717 10 NIR 840 40

3.5. In Situ Measurements For the duration of the SARSense ﬂight campaign, a large number of climate, soil, and vegetation parameters were measured, both from already installed operational stations and planned ﬁeld sampling. Due to the complex soil texture and the presence of different crop types, a high number of in situ soil moisture measurements and plant samplings were conducted, simultaneously or close to the SAR recordings. At the same time, permanently installed measuring stations with a high temporal resolution provided continuose data for the entire period of the SARSense campaign (from June to August 2019) at selected locations. Thanks to the combination of these two datasets, both the temporal and spatial variability of meteorological, soil, and plant parameters could be captured satisfactorily.

3.5.1. Soil Moisture For the soil moisture measurements, a Hydra Probe II was used. It is a coaxial impedance dielectric sensor, measuring both components of the complex dielectric permittivity, allowing simultaneously measuring soil moisture and soil electrical conductivity (EC) [59]. The sensing volume is 5.7 cm by 3.0 cm, where 5.7 cm is the integrated soil moisture sensing depth. The accuracy for soil moisture is ±3%, for EC ± 0.005 S/m and Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 28

soil water content in soil depths of 0.1 m, 0.3 m, and 0.5 m. The data from the fixed installed TERENO measuring stations are publicly available and can be found at https://www.tereno.net/ddp.

3.5.2. Plant Sampling On the 25 of June, a total of 45 plant samples were taken for potato (F11), sugar beet (F04, F01, F47), wheat (F05, F22b, F08), barley (F33, F48b), rapeseed (F53), rye (F27, F49) and corn (F03, F24b, F06). On the 7 August, a total of 22 samples were taken for potato (F11), sugar beet (F04, F01, F47) and corn (F03, F24b, F06), where the other crops were already harvested. Within a 40 cm by 40 cm square, whole plants were harvested at each location and a representative plant was selected and sealed within a plastic bag for later laboratory measures. Furthermore, for the determination of the Chlorophyll and Carote- noid content, fresh green leaves were sampled. Using a leaf tissue puncher, five to ten leaf disks with a diameter of 9 mm were randomly punched out of the upper green leaves of a plant, each weighting between 10 mg and 20 mg. The leaf disks were transferred into 2 mL microcentrifuge tubes, immediately frozen in liquid nitrogen and transported to the FZJ. On the field site, the mean plant height (five plants at each location), the development stage of the plants according to the German Federal Biological Research Centre for Agri- culture and Forestry, Federal Plant Variety Office and Chemical Industry (BBCH), LAI Remoteand Sens. 2021chlorophyll, 13, 825 content were measured. The LAI was determined using a SunScan plant 10 of 29 canopy analyzer, which measures the photosynthetically active radiation (PAR) in vegetation canopies with a 1 m probe, compared to the reference PAR measured by a BF5 reference PAR sunlight sensor for[66]. soil The temperature chlorophyll± 0.1 content◦C. As demonstratedwas measured in previousby a SPAD-502Plus research, the HydraChlo- Probe rophyll meter, calculatingmeasurements the mean are of precise ten measurements and accurate in at ﬂuids each with location known [67]. dielectric properties and Within three dayshighly from correlated the field with sampling soil moisture,, the indicating sealed plant the potential samples of thewere instrument processed for quantitative measurement [60]. The mobile soil moisture measurements were performed using in the laboratory, determiningthe Mobile Hydrathe LAI, Set, fresh which total is equipped biomass, with dry an internalweight, GPS and device canopy and water smartphone content for leaves andconnection. stems separately. The measured Here variables, the atfresh the ﬁeldsweight are of sampling each plant time, coordinates,is deter- soil mined, and the LAI istemperature, calculated soil consecutively moisture, bulk using EC raw, a Li-3200 bulk EC Area TC (thermal Meter correction), (LiCor, Lincoln, pore water EC, NE, USA). After leavingand real the and plants imaginary in a partdrying of the oven dielectric at 65 permittivity °C for five (raw to andsix TC).days, In total,the dry more than 5000 measurements were collected with four Mobile Hydra Probe II during the airborne weight was measured,SAR and acquisition the canopy dates water for the content whole Selhausen was determined area (Figure by6). subtracting In addition todry the soil weight from the freshparameters, weight. the plant height was sampled and logged at these locations as well.

FigureFigure 6. 6.Soil Soil moisture moisture sampling sampling points forpoints the 21 for June the (left) 21 andJune plant (left) sampling and plant points sampling for the 25 June points and 7for August the (right). 25 June and 7 August (right).For the 27 June and 8 August, a cosmic-ray neutron sensing rover was used for measuring soil moisture. With the mobile sensor, a large spatial area can be covered 4. Methods within a short time, whereby the individual measurements represent an area of ~8 to In order to provide18 hectare recommendations [61] and do not represent for the ROSE-L a temporal satellite course. Asmission, the measurement the potential uncertainty of soil moisture depends on the number of measured neutrons, highly sensitive devices of L-band SAR dataare for needed soil surface [62]. Therefore, and vegetation the Forschungszentrum parameter retrieval Jülich (FZJ) and cosmic its synergy rover uses an 10 effects through potentialarray combination of 9 detector units, with each C-band holding SAR four dataBF from3-filled existing tubes, summing missions up like to a total Sentinel-1 need to beof evaluated. 36 cosmic-ray Special neutron focu probes.s lies The on presentedthe use of data L- reliesand C-band on five detector SAR data units that for soil moisture retrievalwere measuring at high resolution epithermal neutronsas well atas the the time, added three value of which of wereL-band mounted SAR verticallyin in the car, whereas two were mounted horizontally. The recording interval was set to 10 seconds. [63]. The measurement of soil moisture with cosmic-ray neutron sensing relies on the inverse dependence of above-ground epithermal neutrons (energy range from ~0.2 eV to 100 keV) on the environmental water content in a footprint of 130 m to 240 m radius and 15 cm to 83 cm penetration depth [61]. The measured neutron counts were converted to soil moisture using the approach developed by Desilets et al. [64], which requires the calibration of the measured epithermal neutron intensity to soil moisture within the footprint. This was done during earlier experiments using the measurement from four other cosmic-ray neutron probes stationed in the Rur catchment [35]. In addition, a permanent SoilNet wireless sensor network consisting of five profiles (depths of −0.01, −0.05, −0.1, −0.2, −0.5, and −1 m) is operated in field F11 to measure in situ soil moisture and temperature with SMT100 sensors (Trübner Precision Instru- ments) [35]. The SMT100 sensor measures the transit time of an electromagnetic pulse in a 30 mm wide and 120 mm long transmission line to determine soil moisture. Due to sensor specific calibration the accuracy is for soil moisture is 1–2 Vol.% and for soil temperature ± 0.2 ◦C[65]. Due to the dry conditions, field F11 was irrigated multiple times by the farmer Remote Sens. 2021, 13, 825 11 of 29

during the SARSense campaign period, with an irrigation also taking place on 27 June during an airborne SAR recording. Furthermore, 18 lysimeters (UMS GmbH) are installed on field F10, near real-time measuring the soil matrix potential, soil temperature, soil heat flux and soil water content in soil depths of 0.1 m, 0.3 m, and 0.5 m. The data from the fixed installed TERENO measuring stations are publicly available and can be found at https://www.tereno.net/ddp.

4. Methods In order to provide recommendations for the ROSE-L satellite mission, the potential of L-band SAR data for soil surface and vegetation parameter retrieval and its synergy effects through potential combination with C-band SAR data from existing missions like Sentinel-1 need to be evaluated. Special focus lies on the use of L- and C-band SAR data for soil moisture retrieval at high resolution as well as the added value of L-band SAR in addressing current EO measurement gaps (soil moisture, vegetation biomass, etc.) and enhanced continuity together with other missions such as Sentinel-1. The first step is to compare the airborne data with the corresponding satellite data for each flight track to assess their temporal consistency and to estimate the influence of the makeshift calibration (see Section 3.1). In the next step, the sensitivity of L- and C- band to in situ measured changes of soil moisture, plant height, vegetation water content (VWC) as well as UAS- based Normalized Difference RedEdge index (NDRE) for potato, sugar beet, wheat, and barley fields within the Selhausen test site is analyzed.

4.1. In Situ Pre-Processing In the first step, the in situ data was filtered, using the reliability flag (Data_reliability = 0) as well as soil moisture values with 0.0 vol.% were masked as Not a Number (NaN) Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 28

addressing current EO measurement gaps (soil moisture, vegetation biomass, etc.) and enhanced continuity together with other missions such as Sentinel-1. The first step is to compare the airborne data with the corresponding satellite data for each flight track to assess their temporal consistency and to estimate the influence of the makeshift calibration (see Section 3.1). In the next step, the sensitivity of L- and C- band to in situ measured changes of soil moisture, plant height, vegetation water content (VWC) as well as UAS- based Normalized Difference RedEdge index (NDRE) for potato, sugar beet, wheat, and barley fields within the Selhausen test site is analyzed.

4.1. In Situ Pre-Processing Remote Sens. 2021, 13In, 825 the first step, the in situ data was filtered, using the reliability flag (Data_reliability 12 of 29 = 0) as well as soil moisture values with 0.0 vol.% were masked as Not a Number (NaN) and not considered in further analysis. The in situ soil moisture data was collected as up to three individualand measurements not considered close in further to each analysis. other The (within in situ 1 soil m²) moisture for each data measuring was collected as up location. Therefore,to threemeasurements individual at measurements related points close were to eachaveraged, other (withinand a new 1 m2 point) for each was measuring defined, located inlocation. the center Therefore, of these measurements points. To extract at related the pointspolarimetric were averaged, SAR data and at athese new point was locations, the pointsdeﬁned, were locatedbuffered in theto a center circle of with these an points. 11 m Toradius. extract the polarimetric SAR data at these locations, the points were buffered to a circle with an 11 m radius.

4.2. Sigma Nought 4.2. Sigma Nought The airborne backscatteringThe airborne intensity backscattering 𝜎 was intensity calculatedσ0 was from calculated the SLC from data the by: SLC data by:

q 𝜎 20 𝑙𝑜𝑔0 𝑖 𝑗 2 2 (1) σ = 20 × log10 i + j (1) with i = real part and j = imaginary part of the SLC image, following the technical specifi- with i = real part and j = imaginary part of the SLC image, following the technical specifica- cations of MetaSensing. In the next step, a Lee filter [68] was applied with a window size tions of MetaSensing. In the next step, a Lee filter [68] was applied with a window size of ten pixels to reduceof ten speckle pixels to noise reduce (Figure speckle 7). noise The (Figure pixel grids7). The were pixel geolocated grids were geolocatedusing the using the additional latitudeadditional and longitude latitude information and longitude within information the SLC within NetCDF the SLC file. NetCDF file.

FigureFigure 7. Comparison 7. Comparison between between unﬁltered unfiltered and speckle and ﬁlteredspeckle SARfiltered image SAR for image C-band for (left) C-band and L-band(left) and (right) for HH polarizationL-band from (right) 19 June.for HH polarization from 19 June.

4.3. Linear Correlation4.3. Linear Correlation To investigate theTo sensitivity investigate of the C- sensitivity and L-band of C- to and changes L-band toin changessoil and in plant soil and parame- plant parameters, ters, the backscatteringthe backscattering signals were signals correlated were to correlated the in situ to measured the in situ soil measured moisture, soil VWC moisture, VWC and plant height. Furthermore, they were compared to UAS-based NDRE, using the and plant height. Furthermore, they were compared to UAS-based NDRE, using the Near- Near-Infrared and Red-Edge bands. To compare them with each other, a linear regression Infrared and Red-Edgeanalysis bands. was performed,To compare where them both with the each coefﬁcient other, a of linear determination regression (R anal-2) and the Root ysis was performed,Mean where Square both Deviation the coefficien (RMSD)t areof determination computed for a linear(R²) and regression the Root of theMean two variables Square Deviation for(RMSD) each crop are and computed band. Here, for R a2 giveslinear the regression proportion of of variancethe two ofvariables the dependent for variable each crop and band.(backscattering Here, R² gives signal), the whichpropor cantion be explainedof variance by of the the linear dependent model with variable the independent variable (surface parameter).

5. Results and Discussion

To provide a first overview of this data set, both the temporal and spatial backscattering behavior of C- and L-band from airborne and space-borne sensors are analyzed. To evaluate the potential and significance of the flight data, the respective tracks were compared with the corresponding satellite data. Moreover, we focused on soil moisture, vegetation height, and VWC, thus addressing the main objectives of the SARSense cam- Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 28

(backscattering signal), which can be explained by the linear model with the independent variable (surface parameter).

5. Results and Discussion To provide a first overview of this data set, both the temporal and spatial backscattering behavior of C- and L-band from airborne and space-borne sensors are analyzed. To evaluate the potential and significance of the flight data, the respective tracks were compared with the corresponding satellite data. Moreover, we focused on soil moisture, veg- Remote Sens. 2021, 13, 825 13 of 29 etation height, and VWC, thus addressing the main objectives of the SARSense campaign. As previous studies have shown, the C- and L-band backscatter coefficients differ in their sensitivity to changes of these parameters, being majorly influenced by the crop type [10,69].paign. In As this previous regard, studies two broad-leaved have shown, the root C- crops and L-band (potato backscatter and sugar coefficients beet) and differ two narrow-leavedin their sensitivity cereals (wheat to changes and of barley), these parameters, were selected being for majorly the analysis. influenced by the crop type [10,69]. In this regard, two broad-leaved root crops (potato and sugar beet) and two narrow-leaved cereals (wheat and barley), were selected for the analysis. 5.1. Temporal Trends of Backscattering Signals from Air- and Space-Borne SAR Data 5.1.To Temporal evaluate Trends the ofquality Backscattering and consistency Signals from of Air- the and airborne Space-Borne data, SAR the Data temporal variation of eachTo flight evaluate track the is qualitycompared and to consistency the satellite of the backscattering airborne data, signal. the temporal For the variation period from theof 1 eachJune flight to 31 track August is compared 2019, the to scene-base the satellited backscattering mean and variance signal. Forare the calculated period from for both thethe airborne 1 June toSAR 31 Augustdata and 2019, the the Sentinel-1 scene-based and mean ALOS-2 and data variance of the are corresponding calculated for both area. By usingthe airbornethe area-wide SAR data mean, and thereducing Sentinel-1 the and influe ALOS-2nces dataof changes of the corresponding in individual area. vegetation By using the area-wide mean, reducing the influences of changes in individual vegetation development of different land cover types (e.g., forest and agricultural land), the effect of development of different land cover types (e.g., forest and agricultural land), the effect of thethe airborne airborne calibration calibration on on backscatter backscatter values values can can be be analyzed. analyzed. Since Since the satellitethe satellite data isdata is dual-polarized,dual-polarized, for for C-band C-band VV VV and VHVH polarization polarization (Figure (Figure8), for8), L-bandfor L-band HH andHH HV and HV polarizationpolarization is isused used (Figure (Figure 99).).

FigureFigure 8. Temporal 8. Temporal behavior behavior of backscattering of backscattering sign signalsals of of C-band C-band air- air- and space-bornespace-borne data data for for the the ﬂight flight tracks tracks A, B,A, and B, and C. C. Concerning the C-band, the mean values of the ﬂight tracks are in general lower than the mean values derived from Sentinel-1. Track A is about −3.23 dB in VV polarization and −5.55 dB in VH polarization, track B −3.88 dB and −6.69 dB, and track C −2.21 dB and −4.24 dB below the satellite data. Comparing the months June and August, smaller deviations can generally be observed in June, with a mean difference of −2.91 dB and −3.38 dB in VV polarization for the tracks A and B and −10.66 dB, −11.50 dB, and −9.90 dB in VH polarization for the tracks A, B, and C, respectively. Only the VV polarization signal of track C, the mean deviation is smallest in August, with −1.1 dB. In August, the larger deviation between airborne and satellite data can be observed for track A and B, with −4.19 dB and −4.94 dB in VV and −16.00 dB and −16.37 dB in VH polarization, respectively, as well as in track C with −13.59 dB in VH polarization. Here, only C band VV polarization has higher deviations in June with −1.43 dB. Looking more closely at the temporal behavior of the mean backscattering signals, the variability within the airborne data is higher than within the satellite data. In the period of the airborne SAR recordings in June and August, the mean VV polarized backscattering signals of the airborne tracks have a range of 7.36 dB (track A), 5.31 dB (track B) and 4.70 dB (track C), whereas the mean VV Remote Sens. 2021, 13, 825 14 of 29

polarized backscattering signals derived from Sentinel-1 have a range of 1.06 dB, 0.90 dB, and 1.06 dB within the corresponding areas. For VH polarization, the mean airborne backscattering signals has a range of 8.12 dB (track A), 6.88 dB (track B), and 3.22 dB (track C), compared to the mean backscattering signals from Sentinel-1, with 1.35 dB, 1.00 dB, and 1.25 dB, respectively. In addition, the higher variability of the airborne data, also the temporal behavior differs considerably from the Sentinel-1 data. This is especially evident Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 28 in June, when the airborne backscattering signals increase and decrease signiﬁcantly and partially behave opposite to the Sentinel-1 backscattering signals.

FigureFigure 9. Temporal 9. Temporal behavior behavior of backscattering of backscattering signals signals of of L-band air-air- and and space-borne space-borne data data for for the the ﬂight flight tracks tracks A, B andA, B C. and C.

The L-band backscattering signals from airborne and ALOS-2 data have the same Concerning the C-band, the mean values of the flight tracks are in general lower than trends as the C-band data discussed before. In general, the HH polarized airborne backscat- thetering mean signals values are derived 5.51 dB from (track Sentinel-1. A), 4.66 dB Track (track A B), is and about 8.01 − dB3.23 (track dB in C) VV below, polarization the andHV −5.55 polarized dB in backscatteringVH polarization, signals track are B 7.29 −3.88 dB dB (track and A), −6.69 7.12 dB, dB (trackand track B), and C − 8.932.21 dB dB and −4.24(track dB C)below below the the satellite ALOS-2 backscatteringdata. Comparing signals the within months the June observation and August, period. Heresmaller the devi- ationsdeviation can generally between the be twoobserved data sets in becomesJune, with particularly a mean difference clear in track of C − in2.91 August, dB and where −3.38 dB in VVthe gappolarization between the for mean the tracks backscattering A and B signals and − is10.66 the largest. dB, −11.50 In this dB, regard, and − the9.90 change dB in VH polarizationin frequency for from the 1400tracks MHz A, inB, Juneand toC, 1300resp MHzectively. in August Only the needs VV to polarization be considered signal as of trackone C, cause the formean this deviation behavior, evenis smallest though in this August, trend is with not as −1.1 prominent dB. In August, in the other the tracks. larger de- Focusing on the temporal variability of airborne and ALOS-2 backscattering signals in viation between airborne and satellite data can be observed for track A and B, with −4.19 June, where both data sets have the most comparable temporal frequency, the airborne dBHH and polarized −4.94 dB backscattering in VV and −16.00 signals dB have and a − range16.37 ofdB 2.81 in dBVH (track polarization, A), 3.06 dB respectively, (track B), as welland as 1.75in track dB (track C with C), − compared13.59 dB toin 1.61VH dB,polarization. 1.08 dB, and Here, 1.30 only dB from C band ALOS-2 VV images,polarization hasrespectively. higher deviations For HV in polarization, June with − the1.43 airborne dB. Looking backscattering more closely signals at havethe temporal a range ofbehav- ior5.93 of the dB mean (track A),backscattering 5.95 dB (track signals, B), and the 2.08 va dBriability (track C) within compared the airborne to 0.78 dB, data 0.80 is dB higher thanand within 0.46 dB the from satellite ALOS-2 data. scenes, In the respectively. period of Likethe airborne the airborne SAR C-band recordings data, alsoin June the and August,airborne the L-band mean dataVV polarized has higher backscattering variability as well signals as a stronger of the airborne de- and increasestracks have of the a range of 7.36backscattering dB (track signalA), 5.31 can dB be (track found B) in and the airborne4.70 dB (track data. TheC), temporalwhereas the behavior mean of VV the polar- airborne backscattering signal is similar to the one observed in the airborne C-band data, ized backscattering signals derived from Sentinel-1 have a range of 1.06 dB, 0.90 dB, and while the ALOS-2 data does not change to an equal extent. 1.06 dBAs within shown the in corresponding the temporal analysis areas. of For the VH C- and polarization, L-band data, the the mean airborne airborne data differ backscat- teringfrom signals the space-borne has a range data bothof 8.12 in absolute dB (track values A), and 6.88 in theirdB (track temporal B), behavior.and 3.22 While dB (track the C), compared to the mean backscattering signals from Sentinel-1, with 1.35 dB, 1.00 dB, and 1.25 dB, respectively. In addition, the higher variability of the airborne data, also the temporal behavior differs considerably from the Sentinel-1 data. This is especially evident in June, when the airborne backscattering signals increase and decrease significantly and partially behave opposite to the Sentinel-1 backscattering signals. The L-band backscattering signals from airborne and ALOS-2 data have the same trends as the C-band data discussed before. In general, the HH polarized airborne backscattering signals are 5.51 dB (track A), 4.66 dB (track B), and 8.01 dB (track C) below, the HV polarized backscattering signals are 7.29 dB (track A), 7.12 dB (track B), and 8.93 dB (track C) below the ALOS-2 backscattering signals within the observation period. Here the deviation between the two data sets becomes particularly clear in track C in August, where the gap between the mean backscattering signals is the largest. In this regard, the

Remote Sens. 2021, 13, 825 15 of 29

satellite-based data show only slight changes in the observed period between individual images, the corresponding airborne data exhibit a very strong change, which is also partly opposite to the space-borne data. This becomes particularly evident in June, when the greatest variability between the scene-based mean values within the airborne data in both C- and L-band is observed, which is not reﬂected in the backscatter values of the satellite data. The strong variability of the backscattering signals is therefore rather caused by the sub-optimal calibration than by changes in soil and plant parameters. Without further processing, a comparison between absolute airborne backscattering signals and surface parameters over multiple dates would lead to biased results. In this regard, we focus on the time series of Sentinel-1 and ALOS-2 data when comparing backscattering signals between C- and L-band to changes in surface parameters using in situ measurements from different dates. The airborne data is used for scene-based (spatial) analysis. In conclusion to the preceding analysis, the main results are the following: • Due to sub-optimal radiometric calibration, both C- and L-band airborne data differ in absolute values and in their temporal behavior from corresponding space borne data. • The use of airborne SAR data from different acquisition dates for analyzing the temporal behavior of surface parameters would lead to biased results.

5.2. Backscattering Signal and Soil Moisture To evaluate the behavior of the backscattering signal with respect to changes in soil moisture, the area-weighted means from backscattering signals in a radius of 11 m around the respective measurement location were correlated with the respective soil moisture values. In this regard, the Sentinel-1 scenes from the 21 and 27 June were correlated with the soil moisture values measured on the corresponding days, both scenes recorded in descending mode with an incidence angle of 43◦ over the Selhausen test site. ALOS-2 scenes from the 22 and 27 June were correlated with the in situ measurements of the 21 and 27 June, with incidence angles of 34◦ and 35◦, both in ascending mode. As the field F11 was irrigated on the 27 June by the local farmer, the highest soil moisture values can be observed on the potato fields with 2 vol.% to 31 vol.%, whereas the other fields have soil moisture values ranging between 1 vol.% and 17 vol.%. development stage of sugar beet in June is between BBCH 35 and 40, for potato 31, for wheat 77 and for barley between 77 and 92. With R2 between 0.00 and 0.42 no or only moderate correlations between backscattering signals and soil moisture are present. At L-band, the highest correlation can be observed in the co-polarized channel for the barley fields, with R2 = 0.42, while the lowest correlation is observed for the sugar beet fields with R2 = 0.00 in both co- and cross-polarized channels (Figure 10; Table4). In C-band, the maximum correlation can be observed in the co-polarized channel for the potato fields with R2 = 0.35, whereas the co-polarized channel for the sugar beet fields is uncorrelated with R2 = 0.00. It should be noted here that the data for the potato fields refer to only two individual fields and thus a smaller number of SAR signals are correlated with the soil moisture data, which at the same time show the highest variability. In this sense, the observed correlation is less significant compared to the other studied fields. Moreover, the directional scattering caused by the periodic tillage structure of potato fields can lead to an increase of the backscattering signal up to 3 dB, depending on the row direction, influencing the comparability of the backscattering signals for the two observed potato fields [70]. While most correlations are positive, the C-band backscattering signal in wheat correlates negatively with soil moisture in both channels. Here, the cross-polarized channel with R2 = 0.28 reaches the second highest value at C-band, while the co-polarized signal correlates with R2 = 0.18. Looking at the plant height at the in situ measuring soil moisture sites, most of the wheat plants range between 60 cm to 100 cm. Correlating the backscattering signal to these plant heights, no significant negative correlation can be observed, with R2 = 0.07 in co- and R2 = 0.04 in the cross-polarized channel. In this regard, different plant heights related to soil moisture are not likely causing this behavior. A decreasing trend of backscattering signal in elongated, Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 28

in the co-polarized channel for the potato fields with R² = 0.35, whereas the co-polarized channel for the sugar beet fields is uncorrelated with R² = 0.00. It should be noted here that the data for the potato fields refer to only two individual fields and thus a smaller number of SAR signals are correlated with the soil moisture data, which at the same time show the highest variability. In this sense, the observed correlation is less significant compared to the other studied fields. Moreover, the directional scattering caused by the periodic tillage structure of potato fields can lead to an increase of the backscattering signal up to 3 dB, depending on the row direction, influencing the comparability of the backscattering signals for the two observed potato fields [70]. While most correlations are positive, the C- band backscattering signal in wheat correlates negatively with soil moisture in both channels. Here, the cross-polarized channel with R² = 0.28 reaches the second highest value at C-band, while the co-polarized signal correlates with R² = 0.18. Looking at the plant height at the in situ measuring soil moisture sites, most of the wheat plants range between 60 cm to 100 cm. Correlating the backscattering signal to these plant heights, no significant neg- Remote Sens. 2021, 13, 825 16 of 29 ative correlation can be observed, with R² = 0.07 in co- and R² = 0.04 in the cross-polarized channel. In this regard, different plant heights related to soil moisture are not likely causing this behavior. A decreasing trend of backscattering signal in elongated, narrow-leaved plantsnarrow-leaved like wheat plants and likebarley, wheat caused and barley,by high caused absorption by high rates absorption of densely rates arranged of densely thin plantarranged stems, thin is plantpreviously stems, observed is previously with incr observedeasing with biomass increasing or VWC biomass in X-, C-, or and VWC L-band in X-, [71–73].C-, and L-bandIn this respect, [71–73]. increasing In this respect, soil mois increasingture could soil be moisture accompanied could by be increasing accompanied water by contentincreasing of the water vegetation, content of leading the vegetation, to an atte leadingnuation to of an the attenuation soil scattering of the component soil scattering and thuscomponent to an absolute and thus reduction to an absolute of the backsca reductionttering of the signal. backscattering Increasing backscattering signal. Increasing sig- nalsbackscattering from drying signals soil at from very drying low soil soil moistu at veryre lowconditions soil moisture due to conditionsincreasing duesubsoil to increas- reflec- tance,ing subsoil as observed reﬂectance, by Morrison as observed et al. by [74], Morrison is not etyet al. described [74], is not for yet soils described with vegetation for soils withcover. vegetation cover.

Figure 10. Scatter plots between soil moisture and backscattering signal from co- and cross-polarized channels of C- and L-band satellite data.

Table 4. The coefﬁcient of determination (R2) and Root Mean Square Deviation (RMSD) of the linear regression between backscattering signal and soil moisture.

Crop. C-Band VV C-Band VH L-Band HH L-Band HV R2 0.00 0.03 0.00 0.00 Sugar Beet RMSD 1.15 0.67 6.05 5.25 R2 0.35 0.20 0.05 0.32 Potato RMSD 0.54 0.64 3.56 4.57 R2 0.18 0.28 0.31 0.07 Wheat RMSD 0.95 1.16 2.28 2.07 R2 0.09 0.05 0.42 0.06 Barley RMSD 4.22 3.02 6.86 7.23 Remote Sens. 2021, 13, 825 17 of 29

Considering the RMSD, L-band tends to have significantly higher variance within the backscattering signal than C-band, with a mean RMSD of 5.75 dB compared to 1.54 dB. Here, larger RMSD are observed in the co-polarized channels, with a mean difference to the cross-polarized channels of 0.41 dB at L-band and 0.34 dB at C-band. The greatest difference in RMSD between C- and L-band can be observed in sugar beet fields. Here the L-band backscattering signals are much more scattered and appear randomly distributed around a fixed level. Due to the deeper penetration into the vegetation cover and soil layer, the L-band contains information not only from surface backscattering, but also from backscattering of the fleshy taproots, which are shallowly buried in the soil and have a high water content of up to 80% [75]. This likely causes a worse correlation between L-band backscattering and soil moisture compared to other crops. Looking at the correlations of both polarizations, it can be observed that in L-band al- ways one polarization shows a significantly higher correlation, except for sugar beet. In this regard, the co-polarized signal from the narrow-leaved crops wheat and barley has significantly higher R2 values (0.31 and 0.42) than the cross-polarized signal (0.07 and 0.06), as observed in previous studies [76]. In the potato plant with broad leaves, the trend is reversed, with R2 = 0.32 in the cross-polarized signal and R2 = 0.05 in the co-polarized signal. Here the C-band tends not to have such large differences between cross- and co-polarization. At the potato and wheat fields, where the highest correlations are observed, they differ significantly less. For potato, R2 = 0.35 in the co-polarized signal and R2 = 0.20 in the cross-polarized signal, while for wheat R2 = 0.18 and R2 = 0.28, respectively. This trend can also be observed in the slopes of the linear regressions, where the values in the L-band between the co- and cross-polarization signals differ more than at the C-band. Excluding the sugar beet, where no correlation can be observed in both bands, the mean difference between the slopes of co- and cross-polarized channel for each crop at L-band is 0.26, whereas the difference at the C-band is only 0.04. The larger differences between the co- and cross-polarized channels at the L-band indicate that two different scattering mechanisms are measured in each polarization, whereas at the C-band in both polarizations rather only one scattering mechanism is prominent. Since the L-band waves should penetrate deeper into the vegetation layer, the co-polarized signal contains more of the surface backscattering signal, whereas the cross-polarized signal measures more volume backscattering [77]. The C-band, with a wavelength about four to five times shorter, penetrates less into the vegetation layer than L-band and, therefore, might contain only small signal contributions of soil surface scattering. In this regard, both co- and cross-polarized channels contain mainly the same information from vegetation volume scattering [10]. Summing up the results: • C- and L-band do not show any correlation for sugar beet. • The co-polarized L-band signal has the highest correlation to soil moisture regarding the narrow-leafed crops. • Two different scattering mechanisms are measured with co- and cross-polarization at L-band, while only one scattering mechanism is prominent at C-band.

5.3. Backscattering Signal and Plant Parameters To evaluate the behavior of C- and L-band backscattering on changes in plant parameters, the VWC and plant height measurements from the 25 June and 7 August 2019 were compared to the signals from the co- and cross-polarized channels (VV and VH for C-band, HH and HV for L-band). As the recordings only partially match the in situ measuring dates, Sentinel-1 scenes from the 26 June and the 7 August as well as the ALOS-2 scene from the 27 June were correlated. Since there is a ten-day difference between in situ measurements and ALOS-2 recordings in August, these data were not included due to temporal decorrelation. As wheat and barley were already harvested in August, information on plant parameters was only measured for sugar beet and potato ﬁelds this month. The VWC is between 72% for sugar beet, between 43% and 60% for wheat, 19% and 43% for barley, and for potato the VWC is between 71% and 81%. For plant height, sugar beet was Remote Sens. 2021, 13, x FOR PEER REVIEW 18 of 28

• The co-polarized L-band signal has the highest correlation to soil moisture regarding the narrow-leafed crops. • Two different scattering mechanisms are measured with co- and cross-polarization at L-band, while only one scattering mechanism is prominent at C-band.

5.3. Backscattering Signal and Plant Parameters To evaluate the behavior of C- and L-band backscattering on changes in plant parameters, the VWC and plant height measurements from the 25 June and 7 August 2019 were compared to the signals from the co- and cross-polarized channels (VV and VH for C- band, HH and HV for L-band). As the recordings only partially match the in situ measuring dates, Sentinel-1 scenes from the 26 June and the 7 August as well as the ALOS-2 scene from the 27 June were correlated. Since there is a ten-day difference between in situ measurements and ALOS-2 recordings in August, these data were not included due to temporal decorrelation. As wheat and barley were already harvested in August, information Remote Sens. 2021, 13, 825 18 of 29 on plant parameters was only measured for sugar beet and potato fields this month. The VWC is between 72% for sugar beet, between 43% and 60% for wheat, 19% and 43% for barley, and for potato the VWC is between 71% and 81%. For plant height, sugar beet was measured between 20 20 cm cm and 40 40 cm for the C-band and and 30 30 cm and 40 cm for the L-band, potatoes between between 40 40 and and 60 60 cm, cm, wheat wheat between between 55 55 cm cm and and 95 95 cm, cm, and and barley barley between between 65 cm65 cmand and 105 105cm cm(Figure (Figure 11). 11 To). evaluate To evaluate the correlation the correlation between between backscattering backscattering signals signals and plantand plant parameters, parameters, again again linear linear regressions regressions are calculated are calculated and and the therelated related R² and R2 and RMSD RMSD are compared.are compared.

Figure 11. Scatter plots between vegetation water content (left) and plant height height (right) (right) and and back backscatteringscattering signal signal from from co- co- and cross-polarized channels of C-C- andand L-bandL-band satellitesatellite data.data.

Looking at the VWC, C-band tends to be more sensitive to changes in vegetation water content for sugar beet, while especially the co-polarized signal with R2 = 0.64 performs better than the L-band co-polarized signal with R2 = 0.27 (Table5). For potato, L-band tends to be more sensitive, with R2 = 0.24 and R2 = 0.55 for co- and cross-polarized channel in C-band and R2 = 0.27 and R2 = 0.76 at L-band. Apart from the cross-polarized channel within the barley fields, the L-band signal is much more sensitive to changes in VWC for the elongated, narrow-leaved crops, with R2 values between 0.58 and 0.65 compared to R2 values between 0.12 and 0.33. In contrast to soil moisture, negative correlations can be observed for wheat at C-band as well as for barley at C- and L-band. In this regard, the trend that backscattering signals decrease with increasing VWC in elongated plants can be observed in both bands. While at C-band the trend can be observed for VWC values ranging between 20% and 40% (barley) for co-polarization as well as between 40% and 60% (wheat) for both polarizations, at L-band this behavior can be only observed for values between 20% and 40% for cross-polarization in the barley fields. Since the correlation here is made only with five to six in situ measurements (a small set of samples), further investigations need to be done to confirm this observation. The correlation between the backscattering signal and plant height is lower compared to the VWC. For C-band, only the co-polarized signal from the sugar beet field has a significant correlation with R2 = 0.55, while all other regressions have R2 values ranging between 0.00 and 0.25. For L-band, the highest correlation can be observed in the cross- polarized signal from sugar beet field with R2 = 0.41, while the other R2 values range between 0.00 and 0.22 (Table6). Looking at the behavior of the respective polarizations within the bands, the co-polarized signals at C-band and the cross-polarized signals at Remote Sens. 2021, 13, 825 19 of 29

L-band have higher R2 values for the broad-leaved plants. No such trend can be observed in the narrow-leaved crops. However, it should also be pointed out that due to the low number of measurements this observation requires further analysis.

Table 5. R2 and RMSD of the linear regression between backscattering signal and vegetation water content (VWC).

Crop C-Band VV C-Band VH L-Band HH L-Band HV R2 0.64 0.24 0.27 0.01 Sugar Beet RMSD 0.20 0.40 0.65 1.65 R2 0.24 0.55 0.27 0.76 Potato RMSD 0.41 0.36 1.90 0.28 R2 0.33 0.12 0.58 0.65 Wheat RMSD 0.37 0.21 1.95 0.90 R2 0.16 0.63 0.60 0.17 Barley RMSD 1.02 0.50 0.26 0.83

Table 6. R2 and RMSD of the linear regression between backscattering signal and plant height.

Crop C-Band VV C-Band VH L-Band HH L-Band HV R2 0.55 0.25 0.22 0.41 Sugar Beet RMSD 0.22 0.41 0.73 1.24 R2 0.13 0.00 0.00 0.08 Potato RMSD 0.47 0.80 2.61 1.09 R2 0.08 0.11 0.22 0.10 Wheat RMSD 0.51 0.21 3.63 2.30 R2 0.12 0.01 0.05 0.22 Barley RMSD 1.07 1.34 0.62 0.78

With a mean RMSD of 1.27 dB, the L-band is much more scattered around the linear regression models than the C-band with a mean RMSD of 0.52 dB. This trend can also be observed when looking only at the correlations where both C- and L-band are shifted by one day with the in situ measurement. In previous studies, the difference between C- and L-band was observed in wheat, barley, and rapeseed ﬁelds, where the L-band backscattering signal generally has higher standard deviations than the C-band, especially pronounced in the ﬁrst months of the year. This difference is furthermore higher in the VV polarized backscattering signal than in the HH polarized signal [78]. Summarizing the analysis, the main results are the following: • For both C- and L-band, higher correlation can be observed with VWC than plant height. • The attenuation effect of cereals on the backscattering signal is most prominent at the C-band, resulting in negative correlations.

5.4. Backscattering Signal and Interception A scene-based analysis was performed to study the airborne SAR data on the different behavior of C- and L-band cross- and co-polarized channels, comparing only data from simultaneous recordings. Flight track C was used for this purpose, as it has the smallest absolute deviation from satellite data and comparatively smaller temporal, calibration- caused variations than track A and B. During the SAR acquisition on 27 June, the potato ﬁeld F11 was partially irrigated by the local farmer (Figure 12, dashed line). A direct comparison between the backscattering signal from a non-irrigated and irrigated area is possible, sharing the same surface and observation parameters. The ﬁeld F11 is located at the near-range of the airborne observation, with incidence angles ranging between 32◦ Remote Sens. 2021, 13, x FOR PEER REVIEW 20 of 28

observed when looking only at the correlations where both C- and L-band are shifted by one day with the in situ measurement. In previous studies, the difference between C- and L-band was observed in wheat, barley, and rapeseed fields, where the L-band backscattering signal generally has higher standard deviations than the C-band, especially pronounced in the first months of the year. This difference is furthermore higher in the VV polarized backscattering signal than in the HH polarized signal [78]. Summarizing the analysis, the main results are the following: • For both C- and L-band, higher correlation can be observed with VWC than plant height. • The attenuation effect of cereals on the backscattering signal is most prominent at the C-band, resulting in negative correlations.

5.4. Backscattering Signal and Interception A scene-based analysis was performed to study the airborne SAR data on the different behavior of C- and L-band cross- and co-polarized channels, comparing only data from simultaneous recordings. Flight track C was used for this purpose, as it has the smallest absolute deviation from satellite data and comparatively smaller temporal, calibration- caused variations than track A and B. During the SAR acquisition on 27 June, the potato field F11 was partially irrigated by the local farmer (Figure 12, dashed line). A direct com- Remote Sens. 2021, 13, 825 20 of 29 parison between the backscattering signal from a non-irrigated and irrigated area is possible, sharing the same surface and observation parameters. The field F11 is located at the near-range of the airborne observation, with incidence angles ranging between 32° and and43°. 43As◦ the. As backscattering the backscattering signal signal is depending is depending on the on incidence the incidence angle, angle,SAR-specific SAR-speciﬁc rangerange-dependenciesdependencies can usually can usually be observed be observed with dec withreasing decreasing backscattering backscattering intensity related intensity to relatedincreasing to increasing incidence incidenceangles [79,80]. angles As [ 79shown,80]. Asin Figure shown 12, in Figurea reverse 12, trend a reverse is present trend isat presentfield F11 at with ﬁeld F11increasing with increasing backscattering backscattering values related values to related increasi to increasingng incidence incidence angles. angles.Here, the Here, L-band the L-bandHH polarization HH polarization has an hasincrea anse increase of 0.61 ofdB, 0.61 the dB, HV the polarization HV polarization an in- ancrease increase of 0.45 of 0.45dB per dB one per degree one degree of incidenc of incidencee angle angle while while the C-band the C-band HH HHpolarization polarization has hasan increase an increase of 1.02 of 1.02 dB and dB and HV HVpolarization polarization one oneof 0.86 of 0.86 dB. dB.

FigureFigure 12.12. ObservedObserved andand detrendeddetrended rangerange proﬁleprofile (left)(left) ofof ﬁeldfield F11,F11, backscatteringbackscattering signalsignal inin decibelsdecibels (dB)(dB) forfor irrigatedirrigated andand non-irrigatednon-irrigated areaarea (middle)(middle) andand relatedrelated histograms histograms from from both both areas areas (right). (right).

To compare both bands and polarizations, the trend was adjusted using a linear regression model and the backscattering values were leveled to the respective measured mean. Comparing the histograms from detrended backscattering signals, the largest difference between irrigated and non-irrigated areas are observed at L-band, with a difference of 3.27 dB in the cross-polarized and 2.20 dB in the co-polarized channel between the respective mean values. This is in line with the results of Vermunt et al. [81], which measured a difference of 3 dB for cross-polarization and 2 dB for co-polarization after light precipitation events for sweet corn, related to the water stored temporally in the interception. The C-band for the potato field has a difference of 1.98 dB in the cross-polarized and 1.78 dB in the co-polarized channel, being less influenced by the interception as well as both polarizations are behaving more similar. The effects of interception water on backscattering signals for potato were also observed previously, even though the related changes were described significantly lower from X-, C-, and L-band recordings three hours after a precipitation event by Riedel et al. [82]. Here, the time between a precipitation or irrigation event and a SAR observation seems to be crucial. While Ulaby [83] stated, that the effect of precipitation is only visible for about one hour in wheat fields, at least the time in potatoes is likely to be similarly short in this regard. The different behavior of C- and L-band might be again explained by the different penetration depth of the wavelengths. Due to the shallower penetration into the vegetation cover, C-band is sensitive to the water stored on the surface of the vegetation cover after precipitation or irrigation, while the L-band is also sensitive to the interception water stored within the vegetation. In this sense, the simultaneous recording of C- and L-band during or shortly after precipitation events or irrigation can be used to obtain different information Remote Sens. 2021, 13, 825 21 of 29

about the interception storage of agricultural plants. In addition, the relationship between C- and L-band backscatter could be used to distinguish between backscatter changes related to changes in soil moisture or plant water content and changes related to interception shortly after or during precipitation events.

5.5. Backscattering Signal and Normalized Difference Red Edge Index To analyze the behavior of C- and L-band backscattering signals to other remote sensing vegetation indices, it was compared to the NDRE. For the 27 June, UAS multispectral images were compared to the respective co- (HH) and cross-polarized (HV) SAR images from track C at the locations of soil moisture measurements within an 11 m radius. In the first step, both the C- and L-band images were detrended as described before. For this purpose, a uniform forest patch in the north of the scenes was selected, to calculate the underlying calibration-related range-trend and minimize the influence of the different vegetation covers at the Selhausen test site. The forest patch covers the whole range of incidence angles, spanning from the near- to far-range of the scenes. Opposite to the potato field F11, the range-trends are similar in C- and L-band here. The co-polarized C-band signal has a trend of 0.49 dB per one degree of incidence angle, the cross-polarized signal a trend of 0.47 dB. The L-band co-polarized signal has a trend of 0.46 dB, while the cross polarized signal has the smallest trend with 0.35 dB. Equal to the analysis before, the focus was on the sugar beet, potato, wheat, and barley. The NDRE values range from 0.3 to 0.6 for sugar beet, 0.25 to 0.75 for potato, 0.1 to 0.55 for wheat, and 0.1 to 0.45 for barley. In general, for the broad-leaved crops sugar beet and potato, L-band has higher correlations than C-band, while for the narrow-leafed crops C-band has higher correlations (Figure 13; Table7). The highest correlation at L-band can be found in the cross-polarized channel for potato with R2 = 0.64, while the highest correlation at C-band can be found in the co-polarized channel for wheat and barley with R2 = 0.74. The lowest correlation can be found in the cross-polarized channel for sugar beet, with R2 = 0.14 and R2 = 0.18 for C- and L-band. For the L-band, this is consistent with the low correlations observed in the previous analyses in sugar beet, although the correlation of the backscattering signal with NDRE is higher than that of soil and plant parameters. For the narrow-leaved crops, the co-polarized channel has significantly higher R2 values at C-band, while the difference is not as prominent at L-band. The trend of decreasing backscattering signal with increasing VWC at the C-band can also be observed with Negative correlations in the C-band, as observed in the satellite data, also exist for NDRE for the cereals. The negative correlation is found in both wheat and barley fields and in both polarizations, being generally stronger in the wheat as well as the co-polarized channel. In this respect, it the behavior agrees in large parts with the results from the satellite data. In conclusion to the preceding analysis, the main results are • For the broadleaf crops, L-band shows highest correlation with NDRE, while for the narrow-leafed, C-band shows highest correlation. • C-band is highly affected by the attenuation effects of cereals, resulting in negative correlations with NDRE, while L-band is not affected. Remote Sens. 2021, 13, x FOR PEER REVIEW 22 of 28

prominent at L-band. The trend of decreasing backscattering signal with increasing VWC at the C-band can also be observed with Negative correlations in the C-band, as observed in the satellite data, also exist for NDRE for the cereals. The negative correlation is found in both wheat and barley fields and in both polarizations, being generally stronger in the wheat as well as the co-polarized channel. In this respect, it the behavior agrees in large parts with the results from the satellite data. In conclusion to the preceding analysis, the main results are • For the broadleaf crops, L-band shows highest correlation with NDRE, while for the narrow-leafed, C-band shows highest correlation. Remote Sens. 2021, 13, 825 22 of 29 • C-band is highly affected by the attenuation effects of cereals, resulting in negative correlations with NDRE, while L-band is not affected.

FigureFigure 13.13. ScatterScatter plotsplots betweenbetween NormalizedNormalized Difference Difference Red-Edge Red-Edge (NDRE) (NDRE) index index and and backscattering backscatter- signaling signal from from co- and co- and cross-polarized cross-polarized channels channels of C- of and C- L-bandand L-band airborne airborne data. data.

TableTable 7.7.R R²2 andand RMSDRMSD ofof thethe linearlinear regressionregression betweenbetween backscatteringbackscattering signalsignal andand NDRE.NDRE.

CropCrop C-Band C-Band HH HH C-Band C-Band HV HV L-Band L-Band HH HH L-Band L-Band HV HV R2 R² 0.22 0.22 0.140.14 0.240.24 0.180.18 SugarSugar Beet Beet RMSDRMSD 3.753.75 2.242.24 2.372.37 4.704.70 R2 R² 0.34 0.34 0.400.40 0.460.46 0.640.64 PotatoPotato RMSDRMSD 2.642.64 2.262.26 2.442.44 2.762.76 R2 R² 0.74 0.74 0.560.56 0.550.55 0.470.47 WheatWheat RMSDRMSD 0.870.87 1.261.26 5.635.63 6.376.37 R2 R² 0.74 0.74 0.220.22 0.390.39 0.530.53 BarleyBarley RMSDRMSD 0.420.42 0.540.54 5.455.45 6.386.38

6. Conclusions and Outlook With SARSense, we present an extensive multi-frequency SAR time-series dataset, including both air- and space-borne C- and L-band recordings, accompanied by in situ measurements of the soil and plant parameters (e.g., soil moisture and vegetation water content) as well as high resolution multispectral and thermal data from UAS and cosmic- ray neutron sensing. The large variety of crops grown at the Selhausen test site and the high temporal and spatial resolution measurements provide a comprehensive database for SAR-based research on agricultural land, especially under low soil moisture conditions (around 8 to 17 vol.%). Here, both small-scale and temporally short correlations between backscattering signal and surface parameters (e.g., interception) as well as more general correlations (e.g., the difference between narrow and broad leaf crops) can be observed. Furthermore, a detailed analysis between C- and L-band for different crop types is possible, Remote Sens. 2021, 13, 825 23 of 29

indicating characteristic backscattering behavior caused by the shape and habitus of the plants. By this, the SARSense campaign continues and extends previous SAR campaigns over the Selhausen test site to provide in-depth and up-to-date knowledge for ongoing and future SAR missions. The dataset as well as the related campaign report can be accessed at https://earth.esa.int/eogateway/campaigns/sarsense-technical-assistance-for- airborne-measurements-during-the-sar-sentinel-experiment (accessed 20 January 2020). Due to misaligned corner reflectors, no absolute calibration of the airborne SAR data was possible. This is significantly influencing the comparability of the backscattering signals from individual flight tracks. The calibration of the airborne data with the Sentinel- 1 scene could not adequately compensate for the error. In this sense, direct comparison between satellite data and airborne data is not possible without further actions, and inter- scene analysis of airborne data would lead to biased results. However, the response of cross- and co-polarized backscattering signals at C- and L-bands to changes in soil and plant parameters across dates can be studied in detail using satellite Sentinel-1 and ALOS-2 data. By using a scene-based approach to analyze the airborne C- and L-band data, they can be used as well for comparing the backscattering signal to the respective ground and plant parameters measured on the same day, although the scenes need to be detrended from the prevailing incidence angle dependency. The correlations between backscattering signal and surface parameters highly vary between the investigated crops, indicating that no general statement can be made on whether C- or L-band is more sensitive to a respective status or dynamic of a soil or plant parameter. Moreover, neither cross- nor co-polarization is generally performing better in terms of sensitivity to these parameters as this sensitivity is specific for each investigated plant. Overall, during the extremely dry conditions of the campaign period, the lowest R2 values are observed in the correlation of the SAR signal to soil moisture, with R2 values ranging between 0.00 and 0.35 at C-band and 0.00 and 0.42 at L-band. Almost no correlation can be observed for the sugar beet, indicating that no information from surface backscattering is present for C-band while at L-band, the shallow buried sugar beets are leading to a speckle induced decorrelation. As seen in potato, where the C-band has a higher R2 value than L-band in the respective polarization, the backscattering signal reflected by the canopy surface at C-band may sometimes have a stronger correlation to the soil moisture measured by the more deeply penetrating L-band. Since this is of particular interest when using multi-frequency methods for soil moisture estimation on agricultural sites, further research should be conducted to determine for which plants this occurs. For the narrow-leaved plants, wheat and barley, L-band co-polarization has the highest R2 values. In terms of VWC, the L-band has higher correlations for the cereals like wheat and barley, while for the broad-leaved crops, the C-band shows a higher correlation. For the Normalized Difference Red Edge index, the trend is reversed, with higher R2 at C-band for the narrow leaf plants and slightly higher R2 values at L-band for the broadleaf plants. Since NDRE is also positively correlated with VWC, further research examining their interdependencies for these plant types should be conducted to understand this behavior [84]. Negative correlations at C-band can be observed for the narrow-leaved plants for all soil and plant parameters studied, clearly demonstrating the attenuation effect on the backscattering signal within the elongated plants in both vertical and horizontal polarizations also after the heading stage, as observed in previous studies [69]. This should be considered, when estimating soil and plant parameters using change-based methods at C-band, e.g., alpha-approximation method [69]. Regarding the vegetation height, the backscattering signals correlated only very weakly to changes. Only the C-band co-polarized signal for sugar beet is an exception with R2 = 0.56, whereby the correlation is largely caused by the relationship between plant height and VWC. In almost all cases, the backscattering signals of C- and L-band contain a different amount of information about the observed agricultural fields and their individual soil and plant parameters. Therefore, the simultaneous acquisition of C- and L-band SAR data will result in an additional gain in remote sensing of soil and plant. This is of particular interest Remote Sens. 2021, 13, 825 24 of 29

for agricultural sites with different vegetation types and phenologies, where the sensitivity of the C- and L-band to soil and plant parameters differ.

Author Contributions: Conceptualization, D.M. and C.M.; campaign planning, C.M., D.S., A.C., U.R., and C.B.; methodology, D.M. and C.M.; measurements, all authors (except T.J. and A.F.); data curation, D.M., C.M., T.J., A.F., and C.B.; SAR signal analyses, D.M., C.M., T.J., and A.F.; signal to in situ comparisons, D.M., C.M., T.J., and A.F.; scattering scenario interpretation: D.M., C.M., T.J., and A.F.; validation, D.M.; writing—original draft preparation, D.M.; writing—review and editing, all authors.; visualization, D.M. All authors have read and agreed to the published version of the manuscript. Funding: We gratefully acknowledge funding by the European Space Agency (ESA) under Contract No. 4000125444/18/NL/LF and by the German Ministry of Economic Affairs and Energy (BMWi) through the German Aerospace Center for the AssimEO project (50EE1914A/B). UAS as well as in situ monitoring was made possible by the Helmholtz Initiatives Modular Observation Solutions for Earth Systems (MOSES) and Terrestrial Environmental Observatories (TERENO). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the European Space Agency. The data are not publicly available due to 3rd party data policy. Acknowledgments: Special thanks go to European Copernicus Satellite Program for providing free access to Sentinel-1 SAR data; the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) for the ALOS-2 SAR data; MetaSensing for providing the airborne SAR recordings and the FLEXSense campaign (ESA Contract No. 4000125402/18/NL/NA). Conﬂicts of Interest: The authors declare no conﬂict of interest.

Appendix A Remote Sens. 2021, 13, 825 25 of 29

Table 1. Number of SARSense data acquisitions for the period between 17–30 June and 8–10 August 2019.

17 18 19 20 21 22 23 25 26 27 30 7 8 9 10 Date June June June June June June June June June June June August August August August SAR Data C-band airborne 3 3 3 3 3 3 L-band airborne 3 3 3 3 3 3 Sentinel-1 1 1 1 1 1 1 1 1 1 1 1 ALOS-2 1 1 1 UAS Data Mavic Pro RGB 1 1 1 Micasense RedEdge-M 1 1 FLIR VUE Pro R 640 1 1 In-Situ Measurements Soil Sampling 1355 1023 791 802 543 541 Plant Sampling 45 22 Cosmic Ray Rover 2142 1677 Soil Parameters: Date; Latitude; Longitude; Temperature (◦C), Soil Moisture (%), Bulk Electric Conductivity (raw/thermal corrected); Pore Water Electric Conductivity; Dielectric Permittivity Real (raw/thermal corrected); Dielectric Permittivity Imaginary (raw/thermal corrected), Crop Type, Crop Height Date, Plant Species; Field No.; Amount of Plants (40 × 40 cm2); BBCH; Plant Height (cm); SPAD 502; Sun Scan; Fresh Weight Leaves (g); Fresh Weight Stems (g); Plant Parameters: Leaf Area (cm2); Dry Weight Leaves (g); Water Content Leaves (g); Dry Weight Stems (g); Water Content Stems (g); Chlorophyll A+B; Carotinoide Remote Sens. 2021, 13, 825 26 of 29

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